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Record W3131611444 · doi:10.7759/cureus.13335

Evaluation of Radiology Request Forms in a Tertiary Care Hospital: An Audit With a Focus on the Impact of Technological Intervention

2021· article· en· W3131611444 on OpenAlexaff
Muhammad Danish Barakzai, Zara Za Sheer, Muhammad Azeemuddin, Amna Alvi, Noman Khan, Waseem Mehmood Nizamani, Madiha Beg, Saad Siddiqui

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineAuditStandardizationRadiological weaponPsychological interventionRadiologyIntervention (counseling)Quality (philosophy)Medical physicsNursingComputer science

Abstract

fetched live from OpenAlex

Radiology request forms are the basis of communication between referring physicians and radiologists. These are the sole documents on the basis of which a justification to carry out a radiological procedure is carried out. However, across the globe, there is a problem of inadequately filled radiology request forms. Several interventions like standardization and the use of technology have been proposed worldwide to overcome the shortcomings of inadequately filled radiology request forms. We carried out a two-phase audit assessing the impact of a technological intervention on the quality of radiology requests with the results showing marked improvement in key parameters. A subset analysis was also done to highlight the importance of radiology request forms by following the patients' treatment course. The remaining shortcomings highlight the importance of training sessions and refresher courses for junior doctors in order to familiarize them with the importance of adequately filled radiology request forms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.375
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2021
Admission routes1
Has abstractyes

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